High volumes of data are captured, stored, and available for use in various types of decision-making. However, it is often difficult or impossible for human users of such data to interpret and apply the data, and to engineer computers to operate based on the data and in a manner that optimizes use of the available data.
Computers are often used in various types of scheduling operations, and many such scheduling operations are straightforward. In some contexts, however, it is still difficult or impossible to make large-scale, accurate, and/or timely scheduling decisions, particularly when certain scheduling constraints exist, and/or when a large number of scheduling variables are present.
For example, some scheduling data relates to scheduling delivery operations with multiple location variables and associated constraints. In particular, scheduling shipments when multiple delivery depots are available will be subject to such location variables. For example, multiple depots may be available for access and use by one or more delivery trucks that are delivering goods from the multiple depots to one or more locations.